431 Class 25

Thomas E. Love, Ph.D.

2023-12-05

Getting Started

Today’s Agenda
Should I upgrade R to 4.3.2?

Some modeling with Favorite Movies from 2023-10-24.
- Note: I downloaded Google Sheet to an Excel .xlsx file.

Definitely not before you finish Quiz 2.

I wrote the quiz and sketch in 4.3.1.
After that, it’s up to you. If you use version 4.3.1 or later for your Project B, I’m fine. I’ll start caring about 4.3.2 in 432.
You will probably want to update your packages either way.
I have completed the upgrade to 4.3.2. for these slides, as you can see in the Session Information.

Today’s Packages

library(here)
library(readxl)
library(janitor)
library(gt)
library(gtExtras)
library(mosaic)
library(patchwork)
library(naniar)
library(mice)
library(mitml)
library(car); library(GGally)
library(corrplot); library(ggmice) # new packages today
library(broom)

library(xfun)
library(tidyverse)

theme_set(theme_bw())

Ingesting the data

movie_raw <- read_excel(here("c25/data/movies_2023-10-24.xlsx"),
                        na = c("", "NA")) |> # otherwise only "" is recognized
  clean_names() |>
  type.convert(as.is = FALSE) |>   # convert all characters to factors
  mutate(film_id = as.character(film_id), 
         film = as.character(film))

movies <- movie_raw |>
  select(film_id, imdb_pct10, fc_pctwins, rt_audiencescore, 
         ebert, box_off_mult, budget, metascore, bw_rating, imdb_oscars, 
         mentions, dr_love, gen_1, bacon_1, lang_1, 
         drama, comedy, adventure, action, romance, fantasy, 
         sci_fi, crime, thriller, animation, family, mystery, 
         biography, music, horror, musical, war, history,
         sport, western, film)

dim(movies)

[1] 201  36

Quick Check of Ingest

summary(movies)

   film_id            imdb_pct10      fc_pctwins rt_audiencescore
 Length:201         Min.   : 3.80   Min.   :24   Min.   :28.00   
 Class :character   1st Qu.:11.60   1st Qu.:42   1st Qu.:76.00   
 Mode  :character   Median :15.60   Median :52   Median :86.00   
                    Mean   :17.53   Mean   :51   Mean   :81.91   
                    3rd Qu.:22.20   3rd Qu.:60   3rd Qu.:92.00   
                    Max.   :55.00   Max.   :79   Max.   :98.00   
                                                                 
     ebert        box_off_mult         budget            metascore     
 Min.   :1.000   Min.   : 0.0013   Min.   :   200000   Min.   :  9.00  
 1st Qu.:2.875   1st Qu.: 2.6000   1st Qu.: 12000000   1st Qu.: 61.00  
 Median :3.500   Median : 4.7000   Median : 30000000   Median : 72.00  
 Mean   :3.190   Mean   : 8.5418   Mean   : 59242257   Mean   : 71.35  
 3rd Qu.:4.000   3rd Qu.: 9.3000   3rd Qu.: 90000000   3rd Qu.: 84.00  
 Max.   :4.000   Max.   :73.7000   Max.   :356000000   Max.   :100.00  
 NA's   :25      NA's   :20        NA's   :19          NA's   :10      
   bw_rating      imdb_oscars         mentions     dr_love   gen_1  
 Min.   :0.000   Min.   : 0.0000   Min.   :1.000   No :124   F: 45  
 1st Qu.:1.000   1st Qu.: 0.0000   1st Qu.:1.000   Yes: 77   M:156  
 Median :3.000   Median : 0.0000   Median :1.000                    
 Mean   :2.135   Mean   : 0.9849   Mean   :1.249                    
 3rd Qu.:3.000   3rd Qu.: 1.0000   3rd Qu.:1.000                    
 Max.   :3.000   Max.   :11.0000   Max.   :6.000                    
 NA's   :8       NA's   :2                                          
    bacon_1           lang_1        drama            comedy      
 Min.   :1.000   English :177   Min.   :0.0000   Min.   :0.0000  
 1st Qu.:2.000   Japanese:  7   1st Qu.:0.0000   1st Qu.:0.0000  
 Median :2.000   Hindi   :  5   Median :1.0000   Median :0.0000  
 Mean   :1.886   Italian :  2   Mean   :0.5721   Mean   :0.3582  
 3rd Qu.:2.000   Arabic  :  1   3rd Qu.:1.0000   3rd Qu.:1.0000  
 Max.   :3.000   ASL     :  1   Max.   :1.0000   Max.   :1.0000  
                 (Other) :  8                                    
   adventure          action          romance          fantasy      
 Min.   :0.0000   Min.   :0.0000   Min.   :0.0000   Min.   :0.0000  
 1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.0000  
 Median :0.0000   Median :0.0000   Median :0.0000   Median :0.0000  
 Mean   :0.3333   Mean   :0.2537   Mean   :0.1692   Mean   :0.1393  
 3rd Qu.:1.0000   3rd Qu.:1.0000   3rd Qu.:0.0000   3rd Qu.:0.0000  
 Max.   :1.0000   Max.   :1.0000   Max.   :1.0000   Max.   :1.0000  
                                                                    
     sci_fi           crime           thriller        animation      
 Min.   :0.0000   Min.   :0.0000   Min.   :0.0000   Min.   :0.00000  
 1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.00000  
 Median :0.0000   Median :0.0000   Median :0.0000   Median :0.00000  
 Mean   :0.1244   Mean   :0.1045   Mean   :0.1045   Mean   :0.08955  
 3rd Qu.:0.0000   3rd Qu.:0.0000   3rd Qu.:0.0000   3rd Qu.:0.00000  
 Max.   :1.0000   Max.   :1.0000   Max.   :1.0000   Max.   :1.00000  
                                                                     
     family           mystery         biography           music        
 Min.   :0.00000   Min.   :0.0000   Min.   :0.00000   Min.   :0.00000  
 1st Qu.:0.00000   1st Qu.:0.0000   1st Qu.:0.00000   1st Qu.:0.00000  
 Median :0.00000   Median :0.0000   Median :0.00000   Median :0.00000  
 Mean   :0.08955   Mean   :0.0597   Mean   :0.05473   Mean   :0.05473  
 3rd Qu.:0.00000   3rd Qu.:0.0000   3rd Qu.:0.00000   3rd Qu.:0.00000  
 Max.   :1.00000   Max.   :1.0000   Max.   :1.00000   Max.   :1.00000  
                                                                       
     horror          musical             war            history       
 Min.   :0.0000   Min.   :0.00000   Min.   :0.0000   Min.   :0.00000  
 1st Qu.:0.0000   1st Qu.:0.00000   1st Qu.:0.0000   1st Qu.:0.00000  
 Median :0.0000   Median :0.00000   Median :0.0000   Median :0.00000  
 Mean   :0.0398   Mean   :0.02985   Mean   :0.0199   Mean   :0.01493  
 3rd Qu.:0.0000   3rd Qu.:0.00000   3rd Qu.:0.0000   3rd Qu.:0.00000  
 Max.   :1.0000   Max.   :1.00000   Max.   :1.0000   Max.   :1.00000  
                                                                      
     sport            western             film          
 Min.   :0.00000   Min.   :0.000000   Length:201        
 1st Qu.:0.00000   1st Qu.:0.000000   Class :character  
 Median :0.00000   Median :0.000000   Mode  :character  
 Mean   :0.01493   Mean   :0.004975                     
 3rd Qu.:0.00000   3rd Qu.:0.000000                     
 Max.   :1.00000   Max.   :1.000000

Data Cleaning

Let’s convert budget to express it in millions of US dollars
lang_eng should be 1/0 for English (n = 177) vs. Non-English

movies <- movies |>
  mutate(budget = budget / 1000000,
         lang_eng = as.numeric(lang_1 == "English"))

favstats(~ budget, data = movies) |> gt() |> 
  fmt_number(columns = mean:sd, decimals = 2)

min	Q1	median	Q3	max	mean	sd	n	missing
0.2	12	30	90	356	59.24	68.02	182	19

movies |> tabyl(lang_eng, lang_1) |> gt()

lang_eng	Arabic	ASL	Bengali	Danish	English	French	German	Hindi	Italian	Japanese	Mandarin	Norwegian	Persian	Spanish
0	1	1	1	1	0	1	1	5	2	7	1	1	1	1
1	0	0	0	0	177	0	0	0	0	0	0	0	0	0

Which outcome shall we choose?

We’re interested in a percentage measure (0-100) addressing how beloved the movie is, according to an audience.

Variable	NA	Description
`imdb_pct10`	0	% of 10-star public ratings in IMDB as of 2023-09
`fc_pctwins`	0	% of matchups won on Flickchart as of 2023-10
`rt_audiencescore`	0	Rotten Tomatoes Audience Score (% Fresh) as of 2023-10

Which outcome shall we choose?

p1 <- ggplot(data = movies, aes(x = imdb_pct10)) +
  geom_histogram(aes(y = after_stat(density)), binwidth = 2, 
                 fill = "navy", col = "white") +
  scale_x_continuous(breaks = seq(0, 100, by = 10), limits = c(0, 100)) +
  stat_function(fun = dnorm, 
                args = list(mean = mean(movies$imdb_pct10),
                            sd = sd(movies$imdb_pct10)),
                col = "magenta", linewidth = 1.5)

p2 <- ggplot(data = movies, aes(x = fc_pctwins)) +
  geom_histogram(aes(y = after_stat(density)), binwidth = 2, 
                 fill = "navy", col = "white") +
  scale_x_continuous(breaks = seq(0, 100, by = 10), limits = c(0, 100)) +
  stat_function(fun = dnorm, 
                args = list(mean = mean(movies$fc_pctwins),
                            sd = sd(movies$fc_pctwins)),
                col = "magenta", linewidth = 1.5)

p3 <- ggplot(data = movies, aes(x = rt_audiencescore)) +
  geom_histogram(aes(y = after_stat(density)), binwidth = 2, 
                 fill = "navy", col = "white") +
  scale_x_continuous(breaks = seq(0, 100, by = 10), limits = c(0, 100)) +
  stat_function(fun = dnorm, 
                args = list(mean = mean(movies$rt_audiencescore),
                            sd = sd(movies$rt_audiencescore)),
                col = "magenta", linewidth = 1.5)

p1 / p2 / p3

Which outcome shall we choose?

df_stats(~ imdb_pct10 + fc_pctwins + rt_audiencescore, data = movies) |>
  gt() |> fmt_number(columns = min:sd, decimals = 1)

response	min	Q1	median	Q3	max	mean	sd	n
imdb_pct10	3.8	11.6	15.6	22.2	55.0	17.5	9.1	201
fc_pctwins	24.0	42.0	52.0	60.0	79.0	51.0	12.6	201
rt_audiencescore	28.0	76.0	86.0	92.0	98.0	81.9	13.5	201

Available Predictors for `fc_pctwins`

str(movies |> select(-fc_pctwins))

tibble [201 × 36] (S3: tbl_df/tbl/data.frame)
 $ film_id         : chr [1:201] "M001" "M002" "M003" "M004" ...
 $ imdb_pct10      : num [1:201] 37.3 27.2 14.9 30.1 22.1 17.1 22.7 22.5 21.2 32.4 ...
 $ rt_audiencescore: int [1:201] 93 92 69 89 84 81 94 95 82 92 ...
 $ ebert           : num [1:201] NA 4 2.5 4 4 2.5 4 4 4 2.5 ...
 $ box_off_mult    : num [1:201] 9.1 NA 1.8 5.5 NA 7.3 9.7 2.9 12.3 7.9 ...
 $ budget          : num [1:201] 6.63 NA 30 12 NA ...
 $ metascore       : int [1:201] 67 93 70 84 87 55 89 87 83 78 ...
 $ bw_rating       : int [1:201] 1 3 3 0 3 3 3 3 3 3 ...
 $ imdb_oscars     : int [1:201] 0 2 0 1 0 0 1 8 3 0 ...
 $ mentions        : int [1:201] 1 1 1 1 1 2 1 1 1 1 ...
 $ dr_love         : Factor w/ 2 levels "No","Yes": 1 1 1 2 1 1 1 2 1 2 ...
 $ gen_1           : Factor w/ 2 levels "F","M": 2 2 2 2 1 2 1 2 2 2 ...
 $ bacon_1         : int [1:201] 3 2 2 2 2 2 2 2 2 2 ...
 $ lang_1          : Factor w/ 14 levels "Arabic","ASL",..: 8 9 5 5 13 5 5 5 5 5 ...
 $ drama           : int [1:201] 1 1 1 0 1 1 0 1 0 0 ...
 $ comedy          : int [1:201] 1 0 1 0 0 1 0 0 0 0 ...
 $ adventure       : int [1:201] 0 0 0 1 0 0 0 0 1 1 ...
 $ action          : int [1:201] 0 0 0 0 0 0 0 0 1 1 ...
 $ romance         : int [1:201] 0 0 1 0 0 0 0 0 0 0 ...
 $ fantasy         : int [1:201] 0 0 0 0 0 1 0 0 1 0 ...
 $ sci_fi          : int [1:201] 0 0 0 1 0 0 1 0 0 1 ...
 $ crime           : int [1:201] 0 0 0 0 0 0 0 0 0 0 ...
 $ thriller        : int [1:201] 0 0 0 0 0 0 0 0 0 0 ...
 $ animation       : int [1:201] 0 0 0 0 0 0 0 0 0 0 ...
 $ family          : int [1:201] 0 0 0 0 0 0 0 0 0 0 ...
 $ mystery         : int [1:201] 0 0 0 0 1 0 0 0 0 0 ...
 $ biography       : int [1:201] 0 0 0 0 0 0 0 1 0 0 ...
 $ music           : int [1:201] 0 0 0 0 0 0 0 1 0 0 ...
 $ horror          : int [1:201] 0 0 0 0 0 0 1 0 0 0 ...
 $ musical         : int [1:201] 0 0 0 0 0 0 0 0 0 0 ...
 $ war             : int [1:201] 0 0 0 0 0 0 0 0 0 0 ...
 $ history         : int [1:201] 0 0 0 0 0 0 0 0 0 0 ...
 $ sport           : int [1:201] 0 0 0 0 0 0 0 0 0 0 ...
 $ western         : int [1:201] 0 0 0 0 0 0 0 0 0 0 ...
 $ film            : chr [1:201] "3 Idiots" "8 1/2" "10 Things I Hate About You" "2001: A Space Odyssey" ...
 $ lang_eng        : num [1:201] 0 0 1 1 0 1 1 1 1 1 ...

First Cut: 18 predictors

Quantities

imdb_pct10, rt_audiencescore, box_off_mult, budget, metascore

Counts or Multi-categorical and ordinal

ebert, imdb_oscars, mentions, bw_rating, bacon_1

Binary Categorical

dr_love, gen_1, lang_eng, and
top five genres: drama, comedy, adventure, action, romance

How many predictors can we use?

If we have a linear regression model with 201 observations (at most, some variables are missing, remember), then how many predictors can we realistically fit?

A useful starting strategy when you’re not doing variable selection is that you need at least 15 observations for each coefficient you will estimate, including the intercept.

See https://hbiostat.org/bbr/ Frank Harrell, Biostatistics for Biomedical Research

How Many Predictors (at maximum)?

The model will run, so long as you have more observations than cases, but that’s not a good standard to use.
Bigger samples are better, but sample size is often determined by pragmatic considerations.

A more modern (and complex) answer is found in this Riley et al (2020) BMJ article.

201 / 15 = 13.4 coefficients

That’s really a maximum, though. We’d like to avoid fitting more than perhaps 10 coefficients (including the intercept)…

Each quantitative predictor requires one coefficient
Each binary predictor also requires one coefficient
When treated as multi-categorical, a factor with k levels requires k-1 coefficients

So what shall we do?

Second Cut: 9 predictors

Variable	Type	Description
`imdb_pct10`	Quant	% of 10-star public ratings in IMDB
`rt_audiencescore`	Quant	Rotten Tomatoes Audience Score (% Fresh)
`box_off_mult`	Quant	World Wide Gross Revenue (as multiple of budget)
`metascore`	Quant	Metascore (0-100 scale) from critic reviews
`imdb_oscars`	Quant	# of Oscar (Academy Award) wins
`bw_rating`	Quant	Bechdel-Wallace Test Criteria Met (0-3)
`lang_eng`	Binary	Is primary language English? (1 = Yes, 0 = No)
`drama`	Binary	Is drama listed in imdb_categories? (1 = Yes, 0 = No)
`comedy`	Binary	Is comedy listed in imdb_categories? (1 = Yes, 0 = No)

10 coefficients x 15 = 150 observations needed, at minimum. We have 201.

Create `movies_2`

movies_2 <- movies |>
  select(film_id, fc_pctwins, imdb_pct10, rt_audiencescore,
         box_off_mult, metascore, imdb_oscars, bw_rating,
         lang_eng, drama, comedy, film)

dim(movies_2)

[1] 201  12

Two identifiers (film and film_id)
Our outcome fc_pctwins
Our nine predictors (from previous slide)

How much missingness do we have?

miss_case_table(movies_2) |> gt()

n_miss_in_case	n_cases	pct_cases
0	175	87.064677
1	16	7.960199
2	6	2.985075
3	4	1.990050

miss_var_summary(movies_2) |> filter(n_miss > 0) |> gt()

variable	n_miss	pct_miss
box_off_mult	20	9.9502488
metascore	10	4.9751244
bw_rating	8	3.9800995
imdb_oscars	2	0.9950249

Visualize missing data

plot_pattern(movies_2, rotate = TRUE) ## from ggmice package

Can we assume MCAR and just do a complete case analysis?

mcar_test(movies_2) |> gt()

statistic	df	p.value	missing.patterns
114.8076	83	0.01192347	9

Note that we’re hurt here by reducing our data set to 12 variables, but that’s the way the cookie crumbles.

mcar_test(movies) |> gt()

statistic	df	p.value	missing.patterns
434.3851	414	0.2356999	13

No data-driven variable selection

Why not?

We can’t afford it. Too small a ratio of sample size to predictors.

What about validation?

We don’t want to split our sample, certainly.
There are other methods (coming in 432) which would help, like k-fold cross validation and bootstrap validation.

Can we do multiple imputation from the start?

Let’s create 25 imputations

Visualize missing data

ggmice(movies_imp, aes(x = .imp, y = box_off_mult)) +
  geom_jitter(height = 0, width = 0.2) + labs(x = "Imputation #")

Visualize missing data

ggmice(movies_2, aes(x = box_off_mult, y = fc_pctwins)) +
  geom_point(size = 3)

Visualize imputed data

From all 25 imputations at once

ggmice(movies_imp, aes(x = box_off_mult, y = fc_pctwins)) +
  geom_point(size = 3)

Select Imputation 6

imp6 <- complete(movies_imp, 6) |> as_tibble()
summary(imp6)

   film_id            fc_pctwins   imdb_pct10    rt_audiencescore
 Length:201         Min.   :24   Min.   : 3.80   Min.   :28.00   
 Class :character   1st Qu.:42   1st Qu.:11.60   1st Qu.:76.00   
 Mode  :character   Median :52   Median :15.60   Median :86.00   
                    Mean   :51   Mean   :17.53   Mean   :81.91   
                    3rd Qu.:60   3rd Qu.:22.20   3rd Qu.:92.00   
                    Max.   :79   Max.   :55.00   Max.   :98.00   
  box_off_mult       metascore       imdb_oscars        bw_rating    
 Min.   : 0.0013   Min.   :  9.00   Min.   : 0.0000   Min.   :0.000  
 1st Qu.: 2.8000   1st Qu.: 61.00   1st Qu.: 0.0000   1st Qu.:1.000  
 Median : 4.7000   Median : 71.00   Median : 0.0000   Median :3.000  
 Mean   : 8.5436   Mean   : 71.11   Mean   : 0.9751   Mean   :2.124  
 3rd Qu.: 9.6000   3rd Qu.: 83.00   3rd Qu.: 1.0000   3rd Qu.:3.000  
 Max.   :73.7000   Max.   :100.00   Max.   :11.0000   Max.   :3.000  
    lang_eng          drama            comedy           film          
 Min.   :0.0000   Min.   :0.0000   Min.   :0.0000   Length:201        
 1st Qu.:1.0000   1st Qu.:0.0000   1st Qu.:0.0000   Class :character  
 Median :1.0000   Median :1.0000   Median :0.0000   Mode  :character  
 Mean   :0.8806   Mean   :0.5721   Mean   :0.3582                     
 3rd Qu.:1.0000   3rd Qu.:1.0000   3rd Qu.:1.0000                     
 Max.   :1.0000   Max.   :1.0000   Max.   :1.0000

Correlation Matrix (Imputation 6)

imp6_quants <- imp6 |> select(-film_id, -film)
cor_quants6 <- cor(imp6_quants)

round_half_up(cor_quants6, digits = 2)

                 fc_pctwins imdb_pct10 rt_audiencescore box_off_mult metascore
fc_pctwins             1.00       0.62             0.67         0.24      0.65
imdb_pct10             0.62       1.00             0.61         0.14      0.35
rt_audiencescore       0.67       0.61             1.00         0.22      0.52
box_off_mult           0.24       0.14             0.22         1.00      0.19
metascore              0.65       0.35             0.52         0.19      1.00
imdb_oscars            0.33       0.31             0.22         0.19      0.40
bw_rating             -0.11      -0.04            -0.07         0.05     -0.09
lang_eng              -0.07      -0.17            -0.13         0.05     -0.17
drama                  0.10       0.20             0.23         0.04      0.14
comedy                -0.32      -0.29            -0.21        -0.06     -0.24
                 imdb_oscars bw_rating lang_eng drama comedy
fc_pctwins              0.33     -0.11    -0.07  0.10  -0.32
imdb_pct10              0.31     -0.04    -0.17  0.20  -0.29
rt_audiencescore        0.22     -0.07    -0.13  0.23  -0.21
box_off_mult            0.19      0.05     0.05  0.04  -0.06
metascore               0.40     -0.09    -0.17  0.14  -0.24
imdb_oscars             1.00      0.03     0.12  0.13  -0.19
bw_rating               0.03      1.00     0.01 -0.01   0.04
lang_eng                0.12      0.01     1.00 -0.19   0.08
drama                   0.13     -0.01    -0.19  1.00  -0.26
comedy                 -0.19      0.04     0.08 -0.26   1.00

Correlation Matrix Plot (imp 6)

corrplot(cor_quants6)

Collinearity in Imputation 6

i6_mod <- lm(fc_pctwins ~ imdb_pct10 + rt_audiencescore + 
               box_off_mult + metascore + imdb_oscars + 
               bw_rating + lang_eng + drama + comedy,
             data = imp6)

vif(i6_mod) ## from car package

      imdb_pct10 rt_audiencescore     box_off_mult        metascore 
        1.780058         2.005588         1.087071         1.659157 
     imdb_oscars        bw_rating         lang_eng            drama 
        1.381051         1.020767         1.154302         1.153332 
          comedy 
        1.174549

Any apparent problems with collinearity here?

Scatterplot Matrix (Imputation 6)

ggpairs(imp6_quants)

Build our model in Imputation 6

i6_mod <- lm(fc_pctwins ~ imdb_pct10 + rt_audiencescore + 
               box_off_mult + metascore + imdb_oscars + 
               bw_rating + lang_eng + drama + comedy,
             data = imp6)

glance(i6_mod) |> gt()

r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.6640256	0.6481943	7.471463	41.94396	1.115782e-40	9	-684.3072	1390.614	1426.951	10662.15	191	201

Tidied Coefficients (Imp. 6)

tidy(i6_mod, conf.int = TRUE, conf.level = 0.90) |>
  gt() |> fmt_number(-term, decimals = 3)

term	estimate	std.error	statistic	p.value	conf.low	conf.high
(Intercept)	0.506	4.557	0.111	0.912	−7.026	8.038
imdb_pct10	0.422	0.077	5.449	0.000	0.294	0.550
rt_audiencescore	0.268	0.056	4.835	0.000	0.177	0.360
box_off_mult	0.062	0.048	1.288	0.199	−0.018	0.142
metascore	0.306	0.043	7.052	0.000	0.234	0.377
imdb_oscars	−0.068	0.328	−0.208	0.835	−0.610	0.474
bw_rating	−0.483	0.489	−0.987	0.325	−1.292	0.326
lang_eng	2.742	1.746	1.570	0.118	−0.144	5.628
drama	−2.503	1.144	−2.188	0.030	−4.393	−0.612
comedy	−2.908	1.191	−2.441	0.016	−4.876	−0.939

par(mfrow = c(2,2)); plot(i6_mod); par(mfrow = c(1,1))

Pooled Coefficient Estimates

movies_mods <- with(movies_imp, 
                    lm(fc_pctwins ~ imdb_pct10 + rt_audiencescore + 
                         box_off_mult + metascore + imdb_oscars + 
                         bw_rating + lang_eng + drama + comedy))

summary(movies_mods)

# A tibble: 250 × 6
   term             estimate std.error statistic  p.value  nobs
   <chr>               <dbl>     <dbl>     <dbl>    <dbl> <int>
 1 (Intercept)        0.0133    4.46     0.00297 9.98e- 1   201
 2 imdb_pct10         0.435     0.0756   5.76    3.37e- 8   201
 3 rt_audiencescore   0.252     0.0541   4.66    6.00e- 6   201
 4 box_off_mult       0.0755    0.0472   1.60    1.11e- 1   201
 5 metascore          0.318     0.0406   7.84    3.08e-13   201
 6 imdb_oscars       -0.159     0.321   -0.495   6.21e- 1   201
 7 bw_rating         -0.404     0.480   -0.841   4.02e- 1   201
 8 lang_eng           3.04      1.71     1.78    7.59e- 2   201
 9 drama             -2.08      1.12    -1.86    6.41e- 2   201
10 comedy            -2.46      1.17    -2.11    3.63e- 2   201
# ℹ 240 more rows

Pooled Estimates: \(R^2\) & adjusted \(R^2\)

movie_pool <- pool(movies_mods)

pool.r.squared(movies_mods)

          est     lo 95     hi 95        fmi
R^2 0.6684598 0.5853228 0.7384219 0.02694699

pool.r.squared(movies_mods, adjusted = TRUE)

              est     lo 95     hi 95        fmi
adj R^2 0.6528365 0.5670716 0.7254529 0.02757476

Or, you can just run glance on the pooled results…

glance(movie_pool)

  nimp nobs r.squared adj.r.squared
1   25  201 0.6684598     0.6528365

Pooled Coefficient Estimates

summary(movie_pool, conf.int = TRUE, conf.level = 0.90) |> gt() |>
  fmt_number(-term, decimals = 3)

term	estimate	std.error	statistic	df	p.value	5 %	95 %
(Intercept)	0.323	4.649	0.069	174.894	0.945	−7.366	8.011
imdb_pct10	0.420	0.078	5.400	184.589	0.000	0.291	0.548
rt_audiencescore	0.261	0.056	4.636	180.852	0.000	0.168	0.354
box_off_mult	0.066	0.049	1.357	156.498	0.177	−0.015	0.148
metascore	0.310	0.043	7.133	179.271	0.000	0.238	0.382
imdb_oscars	−0.103	0.330	−0.311	183.990	0.756	−0.648	0.442
bw_rating	−0.367	0.497	−0.738	181.708	0.462	−1.189	0.455
lang_eng	2.995	1.794	1.669	175.651	0.097	0.028	5.961
drama	−2.299	1.146	−2.006	186.254	0.046	−4.194	−0.405
comedy	−2.815	1.195	−2.356	185.783	0.019	−4.791	−0.840

Coefficient Estimates: Imp. 6

tidy(i6_mod, conf.int = TRUE, conf.level = 0.90) |>
  gt() |> fmt_number(-term, decimals = 3)

term	estimate	std.error	statistic	p.value	conf.low	conf.high
(Intercept)	0.506	4.557	0.111	0.912	−7.026	8.038
imdb_pct10	0.422	0.077	5.449	0.000	0.294	0.550
rt_audiencescore	0.268	0.056	4.835	0.000	0.177	0.360
box_off_mult	0.062	0.048	1.288	0.199	−0.018	0.142
metascore	0.306	0.043	7.052	0.000	0.234	0.377
imdb_oscars	−0.068	0.328	−0.208	0.835	−0.610	0.474
bw_rating	−0.483	0.489	−0.987	0.325	−1.292	0.326
lang_eng	2.742	1.746	1.570	0.118	−0.144	5.628
drama	−2.503	1.144	−2.188	0.030	−4.393	−0.612
comedy	−2.908	1.191	−2.441	0.016	−4.876	−0.939

Session Information

session_info()

R version 4.3.2 (2023-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 11 x64 (build 22621)

Locale:
  LC_COLLATE=English_United States.utf8 
  LC_CTYPE=English_United States.utf8   
  LC_MONETARY=English_United States.utf8
  LC_NUMERIC=C                          
  LC_TIME=English_United States.utf8    

Package version:
  abind_1.4-5          askpass_1.2.0        backports_1.4.1     
  base64enc_0.1.3      bigD_0.2.0           bit_4.0.5           
  bit64_4.0.5          bitops_1.0.7         blob_1.2.4          
  boot_1.3-28.1        brio_1.1.3           broom_1.0.5         
  broom.helpers_1.14.0 bslib_0.6.1          cachem_1.0.8        
  callr_3.7.3          car_3.1-2            carData_3.0-5       
  cellranger_1.1.0     cli_3.6.1            clipr_0.8.0         
  codetools_0.2-19     colorspace_2.1-0     commonmark_1.9.0    
  compiler_4.3.2       conflicted_1.2.0     corrplot_0.92       
  cpp11_0.4.7          crayon_1.5.2         curl_5.1.0          
  data.table_1.14.8    DBI_1.1.3            dbplyr_2.4.0        
  desc_1.4.2           diffobj_0.3.5        digest_0.6.33       
  dplyr_1.1.4          dtplyr_1.3.1         ellipsis_0.3.2      
  evaluate_0.23        fansi_1.0.5          farver_2.1.1        
  fastmap_1.1.1        fontawesome_0.5.2    forcats_1.0.0       
  foreach_1.5.2        fs_1.6.3             gargle_1.5.2        
  generics_0.1.3       GGally_2.2.0         ggformula_0.12.0    
  ggmice_0.1.0         ggplot2_3.4.4        ggridges_0.5.4      
  ggstats_0.5.1        glmnet_4.1-8         glue_1.6.2          
  googledrive_2.1.1    googlesheets4_1.1.1  graphics_4.3.2      
  grDevices_4.3.2      grid_4.3.2           gridExtra_2.3       
  gt_0.10.0            gtable_0.3.4         gtExtras_0.5.0      
  haven_2.5.4          here_1.0.1           highr_0.10          
  hms_1.1.3            htmltools_0.5.7      htmlwidgets_1.6.3   
  httr_1.4.7           ids_1.0.1            isoband_0.2.7       
  iterators_1.0.14     janitor_2.2.0        jomo_2.7-6          
  jquerylib_0.1.4      jsonlite_1.8.7       juicyjuice_0.1.0    
  knitr_1.45           labeling_0.4.3       labelled_2.12.0     
  lattice_0.21-9       lifecycle_1.0.4      lme4_1.1-35.1       
  lubridate_1.9.3      magrittr_2.0.3       markdown_1.11       
  MASS_7.3-60          Matrix_1.6-4         MatrixModels_0.5.3  
  memoise_2.0.1        methods_4.3.2        mgcv_1.9.0          
  mice_3.16.0          mime_0.12            minqa_1.2.6         
  mitml_0.4-5          modelr_0.1.11        mosaic_1.9.0        
  mosaicCore_0.9.4.0   mosaicData_0.20.4    munsell_0.5.0       
  naniar_1.0.0         nlme_3.1-163         nloptr_2.0.3        
  nnet_7.3-19          norm_1.0-11.1        numDeriv_2016.8.1.1 
  openssl_2.1.1        ordinal_2022.11.16   paletteer_1.5.0     
  pan_1.9              parallel_4.3.2       patchwork_1.1.3     
  pbkrtest_0.5.2       pillar_1.9.0         pkgbuild_1.4.2      
  pkgconfig_2.0.3      pkgload_1.3.3        plyr_1.8.9          
  praise_1.0.0         prettyunits_1.2.0    prismatic_1.1.1     
  processx_3.8.2       progress_1.2.2       ps_1.7.5            
  purrr_1.0.2          quantreg_5.97        R6_2.5.1            
  ragg_1.2.6           rappdirs_0.3.3       RColorBrewer_1.1-3  
  Rcpp_1.0.11          RcppEigen_0.3.3.9.4  reactable_0.4.4     
  reactR_0.5.0         readr_2.1.4          readxl_1.4.3        
  rematch_2.0.0        rematch2_2.1.2       reprex_2.0.2        
  rlang_1.1.2          rmarkdown_2.25       rpart_4.1.21        
  rprojroot_2.0.4      rstudioapi_0.15.0    rvest_1.0.3         
  sass_0.4.7           scales_1.3.0         selectr_0.4.2       
  shape_1.4.6          snakecase_0.11.1     SparseM_1.81        
  splines_4.3.2        stats_4.3.2          stringi_1.8.2       
  stringr_1.5.1        survival_3.5-7       sys_3.4.2           
  systemfonts_1.0.5    testthat_3.2.1       textshaping_0.3.7   
  tibble_3.2.1         tidyr_1.3.0          tidyselect_1.2.0    
  tidyverse_2.0.0      timechange_0.2.0     tinytex_0.49        
  tools_4.3.2          tzdb_0.4.0           ucminf_1.2.0        
  UpSetR_1.4.0         utf8_1.2.4           utils_4.3.2         
  uuid_1.1.1           V8_4.4.0             vctrs_0.6.5         
  viridis_0.6.4        viridisLite_0.4.2    visdat_0.6.0        
  vroom_1.6.4          waldo_0.5.2          withr_2.5.2         
  xfun_0.41            xml2_1.3.5           yaml_2.3.7

431 Class 25

Getting Started

Today’s Packages

Ingesting the data

Quick Check of Ingest

Data Cleaning

Which outcome shall we choose?

Which outcome shall we choose?

Which outcome shall we choose?

Which outcome shall we choose?

Available Predictors for fc_pctwins

First Cut: 18 predictors

How many predictors can we use?

How Many Predictors (at maximum)?

201 / 15 = 13.4 coefficients

Second Cut: 9 predictors

Create movies_2

How much missingness do we have?

Visualize missing data

Can we assume MCAR and just do a complete case analysis?

No data-driven variable selection

Let’s create 25 imputations

Visualize missing data

Visualize missing data

Visualize imputed data

Select Imputation 6

Correlation Matrix (Imputation 6)

Correlation Matrix Plot (imp 6)

Collinearity in Imputation 6

Scatterplot Matrix (Imputation 6)

Build our model in Imputation 6

Tidied Coefficients (Imp. 6)

Pooled Coefficient Estimates

Pooled Estimates: \(R^2\) & adjusted \(R^2\)

Pooled Coefficient Estimates

Coefficient Estimates: Imp. 6

Session Information

Available Predictors for `fc_pctwins`

Create `movies_2`